Building data models with Snowplow and dbt

The article explores the synergistic use of Snowplow and dbt for building, testing, and deploying data models, emphasizing the advantages of capturing rich, well-structured behavioral data with Snowplow and transforming it using dbt's versatile SQL-based framework. It highlights Snowplow's ability to collect raw, unopinionated data tailored to business needs, while dbt offers enhanced collaboration, robust testing, and documentation capabilities. The text underscores the importance of establishing best practices in data modeling, such as creating a SQL style guide, making strategic decisions early in the modeling lifecycle, building a data dictionary, and considering cost-effective approaches. Additionally, it emphasizes the significance of rigorous testing and integrating data modeling practices within a Continuous Integration workflow for efficient deployment while maintaining clear communication with team members and stakeholders.